Air Pollution Forecasting Research Articles

Periphery Theory and Its Application to Air Pollution Forecast

The periphery phenomena and periphery definition as well as periphery theory are introduced; The basic structure of a periphery （jieke） of system is composed of both a wall, which defends system itself, and a gate ( or passage), through which the exchange between the system and its environment is carried out. In order to describe the periphery mathematically, perimeter set are presented. On the basis two indicant functions: security degree and subsist ratio are suggested. By using air pollution and meteorological data, a case of air pollution forecast is exemplified in detail.

Analyzing and forecasting air pollution concentration in the capital and Southern Thailand using a lag-dependent Gaussian process model.

The air pollution problem has now amassed worldwide attention due to its multifaceted harm to human health. Exploring the concentration of air pollution and improving forecast have important consideration worldwide. In this research, we analyze the air pollution concentration of Southern Thailand and compare it with the central region. Also, we proposed a methodology based on the lag-dependent Gaussian process (LDGP), a Bayesian non-parametric machine learning model, with a stable optimization approach, which is a cluster-based multi-starter technique based on the Nelder-Mead optimizer. This model also provides the confidence band for forecasted values. We also used autoregressive deep neural network (AR-DNN), autoregressive random forest (AR-RF), gradient boosting (GB), and K-nearest neighbors (KNN) models. A comparison of the proposed methodology was performed on the daily air pollution data collected from the southern provinces and also from the capital of Thailand from 1 January 2018 to 31 December 2022. We used well-established performance evaluation measures to compare the performance of the models. To evaluate the bias due to overfit, we performed a tenfold cross-validation for all the pollutants in each region and compared the models to choose the best one. Moreover, we explored the concentration of air pollution in these regions. Results of descriptive analysis revealed that Bangkok had a much higher concentration of air pollution as compared to the southern region. However, the southern region had higher exposure to PM air pollutants as per WHO recommendations and also had higher exposure to O3 and CO levels. The proposed LDGP model outperformed the other machine learning models for forecasting all air pollutants. Hence, it is recommended to be used by experts for further research and studies with different kernel functions. This research is also expected to contribute to local government planning and prevention and worldwide use of the same methodology for the sustainability of public health.

3DVar sectoral emission inversion based on source apportionment and machine learning

Air quality models are increasingly important in air pollution forecasting and control. Sectoral emissions significantly impact the accuracy of air quality models and source apportionment. This paper studied the 3DVar (three-dimensional variational) emission inversion method, which is based on machine learning, and then expanded it to sectoral emission inversion combined with source apportionment. Two machine learning conversion matrices were established to implement this method: a matrix that converts the total pollutant concentration to sectoral source apportionment results and a matrix that converts the sectoral source apportionment results to corresponding emissions. Combined with the O3 (ozone) concentration contributed by VOCs (volatile organic compounds) and NOx (nitrogen oxides) precursors in source apportionment, the inversion ability for O3–NOx–VOCs nonlinear processes was improved. Taking the BTH (Beijing‒Tianjin–Hebei) region from January 15 to 30, 2019, as an example, the results revealed that the regional errors of PM2.5 and O3 in the inversion experiment were reduced by 47% and 45%, respectively, and the temporal errors were reduced by 44% and 16%, respectively.

Development of Air Pollution Forecasting Models Applying Artificial Neural Networks in the Greater Area of Beijing City, China

The ever-increasing industrialization of certain areas of the planet combined with the simultaneous degradation of the natural environment are alarming phenomena, especially in the field of human health. The concentration of particulate matter with an aerodynamic diameter of 2.5 μm (PM2.5) and 10 μm (PM10), nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3) needs constant monitoring, as they consist of the main cause for many diseases. Based on the existence of statutory limits from the World Health Organization (WHO) for the concentration of each of the aforementioned air pollutants, it is considered necessary to develop forecasting systems that have the ability to correlate the current meteorological data with the concentrations of the above pollutants. In this work, the attempt to predict air pollutant concentrations in the wider area of Beijing, China, is successfully carried out using artificial neural network (ANN) models. In the frame of a specific work, a significant number of ANNs are developed. For this purpose, an open-access meteorological and air pollution database was used. Finally, a statistical evaluation of the developed prognostic models was carried out. The results showed that ANNs present a remarkable prognostic ability in order to forecast air pollution levels in an urban environment.

Air Pollution Forecasting in a Regional Context for Sustainable Management

The aim of this research was to develop and apply a statistical model that can be used to forecast long-term daily maximum particulate matter with a diameter of less than 2.5 microns (PM2.5) concentrations. In order to predict the daily maximum PM2.5 concentrations in the northeastern region of Thailand, the extreme value theory was analyzed, and an appropriate distribution model was identified by employing the Generalized Pareto distribution (GPD). The data of daily maximum PM2.5 concentrations during the years 2021–2023 obtained from six stations was used. These stations are located in Khon Kaen, Loei, Nakhon Ratchasima, Nong Khai, Nakhon Phanom, and Ubon Ratchathani provinces. The results of this study reveal that the GPD is appropriate based on the results of Kolmogorov-Smirnov Statistics Test. Estimating the return levels during the following return periods: 2 years, 5 years, 10 years, 25 years, 50 years, and 100 years showed that the area in the upper northeastern region, particularly Loei and Nakhon Phanom, has daily maximum PM2.5 concentrations above 500 micrograms per cubic meter. These results can also be used as information to support decision-making when conducting response planning in high-risk areas, which can be helpful for efficient resource planning and prevention actions. Doi: 10.28991/ESJ-2024-08-05-024 Full Text: PDF

Long-Term Exposure to Nitrogen Dioxide on Cardiovascular Event Risks in Patients with CKD Using Machine Learning-Based Ultra High-Resolution Air Pollution Forecasts

Long-Term Exposure to Nitrogen Dioxide on Cardiovascular Event Risks in Patients with CKD Using Machine Learning-Based Ultra High-Resolution Air Pollution Forecasts

Bibliometric analysis of Indian research trends in air quality forecasting research using machine learning from 2007–2023 using Scopus database

Machine-learning air pollution prediction studies are widespread worldwide. This study examines the use of machine learning to predict air pollution, its current state, and its expected growth in India. Scopus was used to search 326 documents by 984 academics published in 231 journals between 2007 and 2023. Biblioshiny and Vosviewer were used to discover and visualise prominent authors, journals, research papers, and trends on these issues. In 2018, interest in this topic began to grow at a rate of 32.1 percent every year. Atmospheric Environment (263 citations), Procedia Computer Science (251), Atmospheric Pollution Research (233) and Air Quality, Atmosphere, and Health (93 citations) are the top four sources, according to the Total Citation Index. These journals are among those leading studies on using machine learning to forecast air pollution. Jadavpur University (12 articles) and IIT Delhi (10 articles) are the most esteemed institutions. Singh Kp's 2013 "Atmospheric Environment" article tops the list with 134 citations. The Ministry of Electronics and Information Technology and the Department of Science and Technology are top Indian funding agency receive five units apiece, demonstrating their commitment to technology. The authors' keyword co-occurrence network mappings suggest that machine learning (127 occurrences), air pollution (78 occurrences), and air quality index (41) are the most frequent keywords. This study predicts air pollution using machine learning. These terms largely mirror our Scopus database searches for "machine learning," "air pollution," and "air quality," showing that these are among the most often discussed issues in machine learning research on air pollution prediction. This study helps academics, professionals, and global policymakers understand "air pollution prediction using machine learning" research and recommend key areas for further research.

PREDICTION OF AIR POLLUTION BY USING MACHINE LEARNING

Defensive and in charge Nowadays, in many developing and urban areas, the greater air quality has become one of the most important factors in everything. The magnificence of the air is negatively affecting collectibles due to the many tainting methods caused by power consumption, transportation, and other factors. Population growth is a major issue in our nation as it is happening at a rapid pace. This, along with economic expansion, is causing environmental issues in cities, such as water and air pollution. in a portion of the air. Air pollution and pollutants have a direct effect on human health. As is well known, the main sources of pollution include carbon monoxide, nitrogen oxide, particulate matter (PM), so2; etc. A propellant such as gasoline, petroleum, etc. that has not been properly oxidized is producing carbon monoxide. The burning of thermal fuel releases nitrogen oxide (NO), but sulfur dioxide (So2), one of the main air pollutants, is more prevalent and has a greater impact on human health. Multidimensional collisions with location, time, and imprecise boundaries augment the air's dominance. To examine AI-based approaches for air quality prediction is the aim of this enhancement. In this research, we will use a machine learning system to forecast air pollution.

Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.

Air pollution prediction using blind source separation with Greylag Goose Optimization algorithm

Particularly, environmental pollution, such as air pollution, is still a significant issue of concern all over the world and thus requires the identification of good models for prediction to enable management. Blind Source Separation (BSS), Copula functions, and Long Short-Term Memory (LSTM) network integrated with the Greylag Goose Optimization (GGO) algorithm have been adopted in this research work to improve air pollution forecasting. The proposed model involves preprocessed data from the urban air quality monitoring dataset containing complete environmental and pollutant data. The application of Noise Reduction and Isolation techniques involves the use of methods such as Blind Source Separation (BSS). Using copula functions affords an even better estimate of the dependence structure between the variables. Both the BSS and Copula parameters are then estimated using GGO, which notably enhances the performance of these parameters. Finally, the air pollution levels are forecasted using a time series employing LSTM networks optimized by GGO. The results reveal that GGO-LSTM optimization exhibits the lowest mean squared error (MSE) compared to other optimization methods of the proposed model. The results underscore that certain aspects, such as noise reduction, dependence modeling and optimization of parameters, provide much insight into air quality. Hence, this integrated framework enables a proper approach to monitoring the environment by offering planners and policymakers information to help in articulating efficient environment air quality management strategies.

Comparison of the polluted dust plume and natural dust air mass in a spring dust event in Beijing 2023

The occurrence of extreme weather events is becoming more frequent due to global climate change. A long-lasting dust outbreak in the spring of 2023 was triggered by Mongolia cyclones and cold fronts in the dust source areas. In this study, we illustrate the spatial distribution, the transport path of the dust and its influence on the air quality of downstream cities utilizing ground-based and space-borne measurements. Results indicate a more complicated pollution, coexisting of polluted dust stage S1 and pure dust stage S2. In S1, the aerosol was characterized by a dual-layer vertical structure—high extinction coefficient of nearly 1.0 km−1 with a low particle depolarized ratio (PDR) of < 0.1 under 500 m, and a small extinction coefficient of 0.3 km−1 with a high PDR of 0.15 above 500 m. Then the intrusion of the Mongolian cyclone carried new dust plumes on 10 March, suggesting the onset of pure dust period S2. The source transition was also confirmed by the MODIS, CALIPSO and Hysplit observations. The pure Asian dust evolved rapidly with one thickening layer and distributed homogeneously in the boundary layer. The PDR increased significantly to the peak of 0.35 and resulted in the peak PM10 value of > 1300 µg/m3. PM10 positively correlated with trace gases in S1 while varying inversely with the pollution gases in S2. The results help to shed light on the classification of different types of dust and also be useful in developing an effective strategy to forecast air pollution in downstream areas.

AIR POLLUTION PREDICTION USING MODIFIED GRU DEEP LEARNING MODEL

&lt;p&gt;In recent days air quality has been very poor in urban society due to a lot of industries, vehicles and so on. To overcome poor quality, an air quality prediction is much needed to manage an urban area and control pollution. In this article, deep learning (DL) namely a Modified Gated Recurrent Unit (GRU) neural network is used to forecast air pollution. Initially, a new activation function named Dual-Slope Leaky ReLU (DSL-ReLU) is introduced. The DSL-ReLU activation function includes two hyperparameters which control the slopes for positive and negative input values to achieve fine-tuned responses for varying data inputs. Then, the model parameters like learning rate, batch size, and dropout rates are tuned using the Spider Wasp Optimization (SWO) algorithm. The SWO algorithm is based on the female spider wasp’s hunting and nesting behaviour that is applied to solve the optimization issues. This SWO model is used to GRU model parameters and provides robustness in the predictive modelling of air quality monitoring. For validation, the Kaggle data set is used whereas the proposed optimized GRU model considerably improves the accuracy of air pollution forecasts&lt;/p&gt;

Factor-Augmented Autoregressive Neural Network to forecast NOx in the city of Madrid

Air pollution poses a significant threat to public health and the environment in urban areas worldwide. In the context of urban air quality, nitrogen oxides (NOx), comprising nitrogen dioxide (NO2) and nitric oxide (NO), stand out as key pollutants with well-documented adverse effects. The city of Madrid, as the capital and largest urban center of Spain and the third largest of Europe, is no exception to the challenges posed by NOx pollution. Most of the recent literature on forecasting air pollution, and specifically on NOx, is based on the use of Neural Networks (NN). Little is known about the forecasting ability of factor models in this context. The main aim of this paper is to use Factor-Augmented Autoregressive Neural Networks (FA-ARNN-X) to predict future patterns of NOx pollutants in the territorial monitoring stations of Madrid, using lagged NOx values, meteorological variables and latent factors. The main results indicate that the proposed forecasting model provides statistically more accurate predictions of air pollution than its competing benchmarks and should be used by policymakers for more accurate air pollution monitoring.

Forecasting daily PM2.5 concentrations in Wuhan with a spatial-autocorrelation-based long short-term memory model

Accurate daily air pollution forecasts play a pivotal role in enabling government to implement timely emergency responses and helping alert individuals sensitive to air pollution to take preventive measures. The atmospheric continuity fosters spatial correlations among air pollutants at various locations, which is a factor frequently overlooked in contemporary research focused on harnessing data-driven models for air quality prediction. Therefore, this study proposed a Spatial-Autocorrelation-based Long Short-Term Memory (SALSTM) model for the daily forecasting in Wuhan, Hubei Province, China. Using a multivariate prediction approach with daily air pollution data and meteorological data from Wuhan, as well as air pollution data from surrounding cities, from 2021 to 2022 as input, the model was applied for projecting the daily PM2.5 for Wuhan during the year 2023 and conducting accuracy cross-validation. The results were compared with a univariate prediction approach utilizing the Autoregressive Integrated Moving Average (ARIMA) model and the original Long Short-Term Memory (LSTM) model. Furthermore, this study utilized Dynamic Time Warping (DTW) for feature selection in multivariate prediction, comparing the accuracy of prediction results before and after feature selection. Experimental results indicated that the SALSTM model, incorporating the DTW method, achieved a Root Mean Squared Error (RMSE) of 6.92 μg/m3, a Mean Absolute Error (MAE) of 4.07 μg/m3 and a coefficient of determination (R2) of 0.95. Compared to the univariate forecasting method, the three accuracy metrics RMSE, MAE, and R2 have improved by 54.74%, 58.68%, and 37.68%, respectively. In comparison with the original LSTM, the improvement is 23.79%, 30.90%, and 4.40%. In conclusion, the SALSTM model established in this study demonstrates accurate daily forecasting of PM2.5 concentrations.

Examining the relationship between atmospheric pollutants and meteorological factors in Asansol city, West Bengal, India, using statistical modelling.

Meteorological conditions significantly impact ambient air quality in urban environments. This study focuses on Asansol, known as the "Coal City" and the "Industrial Heart of West Bengal," a notable hotspot for air pollution. Despite its significance, limited research has addressed the influence of meteorological factors on key air pollutants in this urban area. From January 2019 to December 2023, this investigation explores the relationships between meteorological parameters (including atmospheric temperature, relative humidity, rainfall, wind speed) and the concentrations of crucial air pollutants (PM2.5, PM10, NO2, SO2). Temporal trends in air pollutant concentrations are also analysed. The Spearman correlation method is used to establish associations between pollutant concentrations and meteorological variables, while multiple linear regression (MLR) models are employed to assess meteorological factors and potential impact on pollutant concentrations. The analysis reveals a decreasing trend in pollutant concentrations in Asansol. Temperature exhibits negative correlations with all pollutants in all seasons except for a positive correlation during the monsoon. Rainfall consistently displays significant negative correlations with pollutants in all seasons. Relative humidity is negatively correlated with pollutants in all seasons, and wind speed, except during the post-monsoon season, shows negative correlations with all pollutants. Linear models excel in predicting particulate matter concentrations but perform poorly in predicting gaseous contaminants. Accounting for seasonal fluctuations and meteorological parameters, this research enhances the accuracy of air pollution forecasting, contributing to a better understanding of air quality dynamics in Asansol and similar urban areas.

Spatially resolved air quality index prediction in megacities with a CNN-Bi-LSTM hybrid framework

Monitoring pollution in megacities is challenging due to their vast urban expanses and numerous pollution sources. To address this, cutting-edge deep learning models are proposed for monitoring, managing, and forecasting pollution levels. In this study, data concerning six pollutants (O3, NO2, CO, SO2, PM2.5, PM10) and Air Quality Index (AQI) values were collected over a five-year period from three distinct zones within the Tehran megacity. The gathered data were incorporated into a dataset comprising 37,044 records, encompassing the concentrations of examined pollutants and the related AQI across various study points. The primary aim was to enhance the precision of deep learning models using a regional strategic separation approach. A frequency domain filter method was employed to address data noise. Four models—Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional Neural Network with LSTM (CNN-LSTM), and Convolutional Neural Network with Bidirectional LSTM (CNN-Bi-LSTM)—were developed to predict AQI values. Evaluation showed disparities in pollution levels among the studied areas. The CNN-Bi-LSTM model exhibited the highest accuracy at 97 %, but challenges in predicting sudden fluctuations reduced accuracy to 95 % in some cases. However, the model consistently captured the underlying pattern of changes. Segregating megacity regions enhances accuracy in forecasting air pollution conditions.

An observational study on the impact of sea-land breeze and low-level jet on air pollutant transport in the Bohai Bay

Nocturnal pollutant concentrations in Bohai Bay often exhibit significant uneven spatial distribution, even under weak atmospheric circulation and stable boundary layer conditions, posing a serious challenge for air pollution forecasting. This study analyzed the causes of inverse changes in nocturnal pollutant concentrations between inland and coastal areas based on a month-long observation campaign on the meteorology and air pollutants in Bohai Bay. The results indicated that the primary cause was the combined influence of sea-land breeze (SLB) and low-level jet (LLJ) under a weak synoptic pattern. Sea breezes (SB) can transport pollutants from the coast to inland areas. The occurrence of land breezes (LB) and southwest-west LLJ at night can lead to the transport of pollutants inland to the coast. The impacts of LLJ on pollutant transport can be observed in three ways. First, the LLJ enhanced the horizontal ventilation in the boundary layer. The horizontal wind speed in the stable boundary layer increased by 2–6 m/s. Second, the LLJ weakened the stability of the stable boundary layer. A thin layer of instability existed between the surface-based and elevated inversion layers. The height of the inversion layer top increased by approximately 200 m, and the strength of the inversion weakened by at least 1 °C. Finally, the LLJ enhanced atmospheric vertical diffusion. The increased turbulent kinetic energy and upward momentum flux in the lower layer of the boundary layer, as well as the pronounced vertical upward motion between the top of the boundary layer and the altitude of the LLJ, contributed to the vertical dispersion of ground pollutants. Pollutants diffusing to the altitude of the LLJ can be transported horizontally to the coastal areas along the southwest-west LLJ.

Dynamic Neural Assimilation: a deep learning and data assimilation model for air quality predictions

Ambient air pollution is known to be a serious issue that has an impact on human health and the environment. Assessing air quality is of the utmost importance to protect human health and the environment. Different tools are available, from monitoring stations to complex models. These systems are capable of accurately predicting air quality levels, but they are often computationally very expensive which makes them poorly efficient. In this paper, we developed a novel model called Dynamic Neural Assimilation (DyNA) integrating Recurrent Neural Networks and Data Assimilation methods to derive a physics-informed system capable of accurately forecasting air pollution tendencies and investigating the relationship with industrial statistics. DyNA is trained in historical data and is fine-tuned as soon as new data comes available. We trained and tested the system on real data provided by the air quality monitoring stations located in Italy from the European Environment Agency and simulated results derived from the air quality modelling system Atmospheric Modelling System-Model to support the International Negotiation on atmospheric pollution on a National Italian level. We analysed air pollution data in Italy from the years 2003–2010 and studied its correlation with nearby industries in some regions where monitoring sensors were available.

Application of the principles of systems analysis for the development of environment for integrated air pollution forecasting

Air pollution forecasting is a well-known scientific problem, but nitrogen dioxide is not often considered as a pollutant in the problem. Traffic, presented by vehicle emissions, is one of the main sources of air pollution by nitrogen dioxide. Some existing investigations take traffic into account as input features for forecasting models but traffic is presented by values measured during previous hours. Therefore, separate models are created and trained for every air pollution measurement station. Trained forecasting models are not used systemati-cally and models for forecasting other indicators are not applied. But air pollution over cur-rent hour is caused by traffic mostly at the same place over the same hour but not over previ-ous hours. Therefore, it is important to investigate how usage of forecasted values of traffic impacts on air pollution by nitrogen dioxide in comparison with usage of traffic values meas-ured over previous hours. This task is considered in the paper. The objective of the study is to investigate impact of system approach on accuracy of forecasting air pollution by nitrogen dioxide. Three types of forecasting models based on long short-term memory were created to investigate such an impact. These types include models with concentration of nitrogen dioxide over previous hours as input features and two types of models with additional input features presented by values of traffic over previous hours and by forecasted values of traffic over output hours correspondingly. The last type of models pre-sented application of systems approach, as it uses other models available in the integrated forecasting environment. Experimental investigation allowed to create and train models as well as to compare the results of the models on the datasets representing car traffic and air pollution in Madrid. Application of the approach based on the principles of systems analysis increases accuracy of forecasting air pollution by nitrogen dioxide. It enables to create inte-grated air pollution forecasting environment, which combines information technologies of traffic and air pollution forecasting.

Estimation of pollutant baseline concentration in Delhi city: An opportunity from coupled effect of rainfall scavenging and lockdown restriction

The accurate measurement of pollutant baseline concentration is a crucial step for robust assessment, prediction and forecasting of air pollution in any region, especially in metro cities that witness extreme high level of pollution. The present study is an attempt to fill this gap and derive the baseline concentrations of two critical air pollutants, i.e., PM2.5 and NOx in Delhi city. The study critically analyzes the data of winter period having consistent rainfall followed by complete weekend lockdown due to COVID-19. The findings indicate that the estimated baseline concentration is in the range of 20–36 μg/m3 and 12–63 μg/m3 for PM2.5 and NOx (NO + NO2), respectively during winter period of year 2022. The estimated baseline levels can be reasonably well compared with other reported values, both in the past studies as well as estimated using other referred methods. The baseline values are found higher in central areas of the city compared to outskirt located stations. The estimated baseline concentrations can be used in models for predicting the actual urban increment (actual increase in the source contribution with respect to the baseline), which will further help in decision making of target-oriented control actions.